Log-logistic distribution maximum likelihood estimation

The maximum likelihood estimate of shape and rate are calculated by transforming the data back to the logistic model and applying mllogis.

Usage

mlllogis(x, na.rm = FALSE, ...)

Arguments

x: a (non-empty) numeric vector of data values.
na.rm: logical. Should missing values be removed?
...: passed to mllogis.

Value

mllogis returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape and rate and the following attributes:

model: The name of the model.
density: The density associated with the estimates.
logLik: The loglikelihood at the maximum.
support: The support of the density.
n: The number of observations.
call: The call as captured my match.call

Details

For the density function of the log-logistic distribution see Loglogistic

References

Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

Dutang, C., Goulet, V., & Pigeon, M. (2008). actuar: An R package for actuarial science. Journal of Statistical Software, 25(7), 1-37.

Examples

mllnorm(precip)
#> Maximum likelihood estimates for the Lognormal model 
#> meanlog    sdlog  
#>  3.4424   0.5247